forked from huawei/mindspore2022
69 lines
3.2 KiB
Python
69 lines
3.2 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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##############export checkpoint file into air, onnx, mindir models#################
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python export.py
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"""
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import argparse
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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore import context, Tensor, nn, load_checkpoint, load_param_into_net, export
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from src.simclr_model import SimCLR, SimCLR_Classifier
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from src.resnet import resnet50 as resnet
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parser = argparse.ArgumentParser(description='SimCLR')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['cifar10'],
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help='Dataset, Currently only cifar10 is supported.')
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parser.add_argument('--device_target', type=str, default="Ascend",
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choices=['Ascend'],
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help='Device target, Currently only Ascend is supported.')
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parser.add_argument("--ckpt_simclr_encoder", type=str, required=True, help="Simclr encoder checkpoint file path.")
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parser.add_argument("--ckpt_linear_classifier", type=str, required=True, help="Linear classifier checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="simclr_classifier", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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if args_opt.device_target == "Ascend":
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context.set_context(device_id=args_opt.device_id)
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if __name__ == '__main__':
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if args_opt.dataset_name != 'cifar10':
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raise ValueError("dataset is not support.")
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width_multiplier = 1
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cifar_stem = True
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projection_dimension = 128
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class_num = 10
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image_height = 32
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image_width = 32
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encoder = resnet(1, width_multiplier=width_multiplier, cifar_stem=cifar_stem)
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classifier = nn.Dense(encoder.end_point.in_channels, class_num)
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simclr = SimCLR(encoder, projection_dimension, encoder.end_point.in_channels)
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param_simclr = load_checkpoint(args_opt.ckpt_simclr_encoder)
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load_param_into_net(simclr, param_simclr)
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param_classifier = load_checkpoint(args_opt.ckpt_linear_classifier)
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load_param_into_net(classifier, param_classifier)
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# export SimCLR_Classifier network
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simclr_classifier = SimCLR_Classifier(simclr.encoder, classifier)
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input_data = Tensor(np.zeros([args_opt.batch_size, 3, image_height, image_width]), mstype.float32)
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export(simclr_classifier, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)
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